Remote sensing change detection plays a crucial part in monitoring the evolution of land cover. Despite remarkable progress in recent years, existing change detection models still struggle with two major challenges that lead to inaccurate change predictions. For one, current models lack sufficient global relation modeling, resulting in coarse boundary prediction and false identification of unwanted variations. For another, bitemporal images often display divergent imaging styles due to lighting, sensor, or seasonal differences, and this cross-temporal style inconsistency would amplify the pseudo-changes caused by non-semantic variations. In this paper, we present the style-alignment and appearance-relation modeling change detection model (STAR-CD) to tackle the above challenges. First, we propose an appearance-relation modeling block (ARMB) to jointly explore semantic differences from both local textures and global structures of remote sensing images, effectively reducing false alarms and refining change boundary predictions. Second, we design a Fourier-inspired style alignment module (FSAM), which suppresses style-induced noise and reveals true changes by texture-style decoupling in the frequency domain. Extensive experiments on the WHU-CD, LEVIR-CD, and CDD datasets demonstrate the superiority of our STAR-CD over state-of-the-art methods. In particular, STAR-CD surpasses previous methods by 2.40% and 4.34% in F1 and IoU on the CDD datasets, respectively. Visual comparisons further validate its strength in predicting precise change boundaries and robustly suppressing pseudo-changes.

Drone-based RGBT person detection promotes various applications such as search and rescue due to its maneuverability. While existing research predominantly concentrates on refining fusion strategies and bolstering learning mechanisms for small objects, the pervasive yet unique occlusion challenge in drone-based RGBT settings remains inadequately addressed. In this work, we address the unique challenge of occlusion in the context of RGBT small object detection, particularly emphasizing its vulnerability and the distinct characteristics it exhibits across different modalities. We propose AODet, a novel Anti-Occlusion Detector meticulously crafted to tackle the challenges posed by occlusion in drone-based RGBT object detection. Our proposed approach significantly improves the detection performance of RGBT small objects, surpassing strong baselines on two large-scale datasets, VTUAV-det and RGBTDronePerson, by 1.30 points and 2.24 points in mAPsand ${\text{mAP}}_{50}^{{\text{tiny}}}$, respectively.

Nighttime aerial tracking is an indispensable step towards around-the-clock real-world applications. However, RGBbased tracking algorithms face significant challenges at night due to their vulnerability to illumination. Observing that different feature channels have varying sensitivity to illumination, we propose to decouple the representation for illuminationsensitive and illumination-insensitive embeddings. We devise a Nighttime aerial tracking scheme via Decouple Representations, termed NiDR, where the Illumination-Invariant Embedding (IIE) module and the Illumination-Sensitive Embedding (ISE) module are designed to decouple representations. We achieve this semantic decoupling by utilizing a pair of normlight and low-light images and regulating the reconstruction and consistency relations between features. Experiments on UAVDark135 exhibit the remarkable performance of NiDR under challenging nighttime scenarios, surpassing the secondbest competitor by a large margin of 3.1% on precision.

Accurate object detection in aerial imagery is crucial across numerous applications. However, haze can significantly degrade the performance of normal detectors, presenting a substantial obstacle in real-world scenarios. Previous solutions often resort to image dehazing as a pre-processing step to enhance image quality for subsequent detection. Despite being logically intuitive, their performance is limited due to the inherent objective mismatch between low-level image restoration tasks and high-level object detection tasks. In this article, we present haze-robust aerial object detection (HRAOD) to directly enhance detection robustness under hazy conditions. HRAOD constructs a clean-to-hazy distillation framework, enabling the detector to “see through haze,” without relying on the explicit image dehazing process. To address the challenge of extracting informative hazy features from blurry and low-contrast hazy images, we introduce a gradient-guided feature imitation method to emphasize the desired objects. Moreover, recognizing that different regions suffer from varying degradation degrees and pose distinct detection difficulties, we further propose a degradation-weighted response distillation method to mimic the normal predictions according to the degradation pattern adaptively. Due to the scarcity of hazy aerial data, we curate two remote sensing hazy aerial datasets, namely DOTA-Haze and SODA-A-Haze, and one drone hazy aerial dataset, DroneVehicle-Haze, for simulation. Extensive experimental results demonstrate the superiority of our method. Specifically, our HRAOD outperforms the state-of-the-art “dehaze + detect” method by 13.1 points in mAP on the DOTA-Haze dataset without incurring additional inference costs. HRAOD also performs favorably against other methods on real-world hazy scenes.

Vanilla aerial trackers exhibit sensitivity to low-light conditions (e.g., nighttime aerial tracking scenario). To mitigate this, existing methods incorporate the light enhancement method as a preprocessing for aerial tracking. Despite the advancements, these approaches are restricted to the disparity in task objectives between the enhancer and tracker. Motivated by the observation that feature channels exhibit varying sensitivity to illumination, we propose to decouple the feature representation into two distinct parts: 1) illumination-invariant feature embedding and 2) illumination-sensitive feature embedding. The former, realized by the illumination invariant embedding (IIE) module, enhances features that remain invariant to illumination changes. Meanwhile, the latter, facilitated by the illumination sensitive embedding (ISE) module, aims to mitigate the negative impact of illumination-sensitive features on tracking performance. Building upon this decoupling strategy, we introduce NiDR, a simple yet effective nighttime aerial tracker. The proposed NiDR exhibits strong performance on three nighttime aerial tracking benchmarks (i.e., UAVDark135, NAT2021, and DarkTrack2021). Notably, it outperforms previous competitors by large margins, e.g., 3.1 points on the UAVDark135 and 2.0 points on the Darktrack2021 in terms of precision for nighttime scenarios.

Thermal infrared (TIR) object detection plays a crucial role in diverse around-the-clock applications, such as search and rescue operations and wildlife protection. Achieving rapid and robust detection of small objects from an aerial perspective is particularly significant in these scenarios. However, the task is compounded by two interrelated challenges, rendering it even more tricky. For one, small objects only occupy a few pixels and contain limited information. For another, TIR sensors are typically low-resolution (LR) due to inherent challenges associated with the imaging mechanism of the TIR spectrum. In contrast, high-resolution (HR) RGB sensors are readily available due to their cost-effectiveness and widespread application. Recognizing the importance of HR information, especially in the context of small object detection, we propose a cross-modality high-resolution knowledge distillation framework (CMHRD), which leverages knowledge from the HR-RGB modality and provides a novel strategy for TIR small object detection. The proposed framework introduces three key components: a super-resolution generative distillation loss for cross-modal high-resolution representation learning, a cross-modality affinity distillation loss to extract scene-level cross-modality information, and a response distillation loss aimed at mimicking the HR prediction. To facilitate research on small object detection with HR-RGB and LR-TIR data, we have curated and annotated two datasets, namely NOAA-Seal and VTUAV-det-small. Experimental results on the NOAA-Seal demonstrate that CMHRD yields significant improvements, achieving a remarkable 6.39 mAP50 increase over a strong baseline without introducing additional computational cost during inference. Experiments on single-category dataset VTUAV-det-small and multi-category dataset RTDOD also show consistent improvements brought by CMHRD. The project is available at https://github.com/NNNNerd/CMHRD.